Learning and Intelligent Optimization

.- Autoregressive RL Approach for Mixed-Integer Linear Programs.

.- Algorithm Configuration in the Unified Planning Framework.

.- Learning to Repair Infeasible$^*$ Problems with Deep Reinforcement Learning on Graphs.

.- Optimal Matched Block Design For Multi-Arm Experiments.

.- CHORUS: Zero-shot Hierarchical Retrieval and Orchestration for Generating Linear Programming Code.

.- Taxi re-positioning considering driver compliance.

.- A Shared Memory Optimal Parallel Redistribution Algorithm for SMC Samplers with Variable Size Samples.

.- A Hybrid Quantum-Inspired and Deep Learning Approach for the Capacitated Vehicle Routing Problem with Time Windows.

.- Multi-Action Sampling with Deep Reinforcement Learning for Traveling Salesman Problem.

.- Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem.

.- Codetector: A Framework for Zero-shot Detection of AI-Generated Code.

.- Pushing the Limits of the Reactive Affine Shaker Algorithm to Higher Dimensions.

.- Convex quadratic programming-based predictors: An algorithmic framework and a study of possibilities and computational challenges.

.- Studies on a Bayesian Optimization Based Approach to Tune Hyperparameters of Matheuristics.

.- Local iterative algorithms for approximate symmetry guided by network centralities.

.- Addressing Over-fitting in Passive Constraint Acquisition through Active Learning.

.- Learning to solve the Skill Vehicle Routing Problem with Deep Reinforcement Learning.

.- CGD: Modifying the Loss Landscape by Gradient Regularization.

.- Data Sampling-driven Adaptive Modification of Bus Routes Under Time-Varying Road Conditions.